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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
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collaboration. The successful candidate will be exposed to and trained in both low-level instrumental modeling, high-level component separation and cosmological parameter estimation, and high-performance
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PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
data-driven modeling, parameter estimation, or model calibration Familiarity with high-performance computing or large-scale simulations Interest in close collaboration with experimental researchers
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statistical physics, solid mechanics, or fluid mechanics Experience with data-driven modeling, parameter estimation, or model calibration Familiarity with high-performance computing or large-scale simulations
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PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
programming skills Desired qualifications: Experience in statistical physics, solid mechanics, or fluid mechanics Experience with data-driven modeling, parameter estimation, or model calibration Familiarity
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physics Solid programming skills Desired qualifications: Experience in statistical physics, solid mechanics, or fluid mechanics Experience with data-driven modeling, parameter estimation, or model